Articles

Interviews

Releases

New Products

Reviews

Editorial

Events

Sponsors

Site Search

Newsletters

Archives

Past Issues

Home

Editors

eDucation

Links

Software

Most existing HVAC systems for commercial buildings aren’t as energy efficient as they could be. This is largely because they are generally fixed systems programmed for static environments. But weather, seasons, and occupancy rates are notoriously dynamic. This discord has led to wasted energy, higher carbon footprints, and unnecessarily large utility bills and maintenance costs. Now, as climate change continues to alter our weather patterns and governments proceed to crack down on GHG emissions, the built environment is under increasing need to innovate.

In the US, a commercial building’s HVAC system accounts for 35-45% of its total energy usage, a large portion of which is generally wasted due to equipment or management inefficiencies. These same buildings also happen to be responsible for 20% of global greenhouse gas emissions, adding to extreme temperatures, an increased demand for HVAC systems, and more stringent energy efficiency policies.

Over the last few years, a plethora of tech companies have concentrated on developing technologies to address these challenges through artificial intelligence, applying machine learning to existing HVAC systems and making them more energy efficient. Innovative solutions, such as connected devices, AFDDs, EMIS, and advanced supervisory controls driven by AI, are gaining in popularity as a means of continuously monitoring, assessing, and adjusting HVAC systems. These solutions draw on untapped efficiency gains and open a pathway to powerful new data insights to better manage ineffective systems.

The impact of these technologies is seen in their significant reduction in total energy costs, improved thermal comfort, and decrease in buildings’ carbon footprints. Additionally, these solutions are cutting down on HVAC equipment’s maintenance and overall life cycle costs by reducing the unit runtime needed to maintain optimal temperatures within a building.

BrainBox AI is a prime example of this kind of technology. Lowering buildings’ total energy costs by up to 25%, reducing their carbon footprints by a 20-40%, increasing occupant comfort by up to 60%, and extending equipment service life by up to 50%, the unobtrusive, autonomous device connects to existing HVAC systems within a matter of hours. Using deep neural AI networks and cloud-based computing, it analyzes internal and external data to predict future temperatures within each zone of a building, allowing it to make pre-emptive micro-adjustments in real-time.

Existing systems’ data also becomes more valuable as BrainBox AI writes back directly to a building’s control systems, rendering it compatible with other machine learning tools. Additionally, the solution supports its clients with a 24/7 monitoring service that keeps track of their building’s system while preventively monitoring for equipment inconsistencies or anticipated issues.